Statistical learning with sparsity: the lasso and generalizations

Media type: Book

Title: Statistical learning with sparsity : the lasso and generalizations

Contributor: Hastie, Trevor [VerfasserIn]; Tibshirani, Robert [VerfasserIn]; Wainwright, Martin J. [VerfasserIn]

imprint: Boca Raton [u.a.]: CRC Press, [2015]

Extent: xv, 351 Seiten; Illustrationen, Diagramme

Language: English

ISBN: 9781498712163

RVK notation: SK 850 : Angewandte Statistik, Tabellen
CM 4000 : Statistik
SK 840 : Spezielle statistische Verfahren
ST 600 : Mathematik, Statistik

Keywords: Statistik > Maschinelles Lernen

Origination:

Footnote: Literaturverz. S. 315 - 335

Description: Front Cover; Contents; Preface; Chapter 1: Introduction; Chapter 2: The Lasso for Linear Models; Chapter 3: Generalized Linear Models; Chapter 4: Generalizations of the Lasso Penalty; Chapter 5: Optimization Methods; Chapter 6: Statistical Inference; Chapter 7: Matrix Decompositions, Approximations, and Completion; Chapter 8: Sparse Multivariate Methods; Chapter 9: Graphs and Model Selection; Chapter 10: Signal Approximation and Compressed Sensing; Chapter 11: Theoretical Results for the Lasso; Bibliography; Back Cover

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized l

Statistical learning with sparsity : the lasso and generalizations

Bookmarks

copies

Bestand der TU Dresden

Central Library

Search in field:

Recently searched for:

Bestand der TU Dresden

Central Library